Society-in-the-loop: programming the algorithmic social contract
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La concentración de la distracción y de la atención genera más datos para ajustar las cajas negras o algoritmos que determinan los intereses del consumidor y, al mismo tiempo, permite un mayor y más refinado control sobre el usuario.
“La concentración de tal distracción y de la atención genera más datos para ajustar las cajas negras o algoritmos que determinan los intereses del consumidor ( Rahwan , 2018) y, al mismo tiempo, permiten un mayor y más refinado control sobre el usuario ( Celis-Bueno , 2017; Franck , 2019a).”
From Redes sociales y consumo digital en jóvenes universitarios: economía de la atención y oligopolios de la comunicación en el siglo XXI (2020) · cites this paper as Rahwan, 2018
The Introduction (p. 1) describes opaque, data-driven algorithms, and §7.3 (p. 7) discusses online ads served using users’ information. Neither passage says that concentrating attention and distraction generates more data to refine algorithms that determine consumer interests. The later claim about greater control over users is attached to other citations, not Rahwan. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe idea of keeping society in the loop could apply also to the meso level, i.e., the organization in which AI is applied.
“There is no reason this idea could not apply also to the meso level, i.e., to the organization in which AI is applied.”
From Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence (2023) · cites this paper as Rahwan (2018)
Section 3 (p. 3) introduces society-in-the-loop as a way to embed society’s values in oversight of AI with broad societal implications. It does not propose applying the framework at the meso level of an organization; that extension is the citing author’s inference. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSome changes result from external factors that include the societal context; others include aspects of inter-organizational collaboration with an ecology of external partners.
“Some changes result from external factors (4a) that include the societal context (Rahwan 2018), others usually include aspects of inter-organizational collaboration (Campion et al. 2020) with an ecology of external partners.”
From Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence (2023) · cites this paper as Rahwan 2018
Rahwan says broad societal values should guide the monitoring of AI systems (Section 6.1, p. 6), and that public-shaped norms should influence how their performance is measured (Section 7.5, p. 8). This supports societal context as an influence on changes to AI governance, but does not establish the broader claim that changes generally result from external factors. The inter-organizational collaboration clause is attributed to another citation, not Rahwan. (LLM, conf=0.78)
Experimental — accuracy not yet measuredOne approach to ensure cultural beliefs and values are present in an AI model is to carefully curate the dataset on which the model is trained.
“One approach is to carefully curate the dataset on which the model is trained 187,188 .”
From Machine culture (2023) · cites this paper as [187]
Wirtz et al. (2020) were only able to identify two frameworks focused on the governance or regulation of AI risks and challenges, namely those of Gasser and Almeida (2017) and Rahwan (2018).
“While looking for frameworks focused on the governance or regulation of AI risks and challenges, Wirtz et al. (2020) were only able to identify two such frameworks, namely those of Gasser and Almeida (2017) and Rahwan (2018).”
From Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021) · cites this paper as Rahwan (2018)
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- Authors
- Rahwan, I.
- Affiliations
- MIT Media Lab (US)
- Corresponding author
- Rahwan, Iyad
- ORCID
- Rahwan, Iyad
- Venue
- Ethics and Information Technology
- Published
- 2017-08-17
- Volume
- 20
- Issue
- 1
- Pages
- 5-14
- ISSN
- 1388-1957 (ISSN-L)
- 1572-8439
- OpenAlex
- W2738428139
- Cited by
- 553
- References
- 54
- Citations by year
- 2026: 101 · 2025: 101 · 2024: 65 · 2023: 70 · 2022: 66 · 2021: 62 · 2020: 49 · 2019: 26 · 2018: 12 · 2017: 1
- FWCI
- 55.24
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Ethics and Information Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- Siegel Family Endowment
- Ethics & Governance of Artificial Intelligence Fund
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- human-in-the-loop, AI governance, AI regulation, algorithmic transparency, algorithmic accountability, algorithmic fairness, stakeholder negotiation, value negotiation, compliance monitoring, interactive machine learning, autonomous machines
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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